Digital rock physics is used for the investigation of oil and gas reservoirs. It involves various mathematical simulations on a digital representation of a rock sample, which is usually obtained with imaging techniques. Focused ion beam scanning electron microscopy (FIB-SEM) tomography provides high-resolution images of sequential layers of a sample, and segmentation of these images is a key stage in the construction of 3D digital rock. Conventional segmentation methods are not applicable for FIB-SEM images due to specific artifacts such as the pore-back effect. We propose a new segmentation algorithm that relies on the marker-controlled watershed, variance filter and morphological operations. The results are validated with the use of manually labelled ground truth data. Furthermore, we develop a new metric for evaluation of segmentation quality. This metric is based on analysis of segmented regions and, in the case of porous media, provides more reliable evaluation than pixel-wise measures.

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Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) tomography provides a stack of images that represent serial slices of the sample. These images are displaced relatively to each other, and an alignment procedure is required. Traditional methods for alignment of a 3D image are based on a comparison of two adjacent slices. However, such algorithms are easily confused by anisotropy in the sample structure or even experiment geometry in the case of porous media. This may lead to significant distortions in the pore space geometry, if there are no stable fiducial marks in the frame. In this paper, we propose a new method, which meaningfully extends existing alignment procedures. Our technique allows the correction of random misalignments between slices and, at the same time, preserves the overall geometrical structure of the specimen. We consider displacements produced by existing alignment algorithms as a signal and decompose it into low and high-frequency components. Final transformations exclude slow variations and contain only high frequency variations that represent random shifts that need to be corrected. The proposed algorithm can operate with not only translations but also with arbitrary affine transformations. We demonstrate the performance of our approach on a synthetic dataset and two real FIB-SEM images of natural rock.